Some control systems used by autonomous or semi-autonomous vehicles aim to avoid accidents of the vehicles by predicting safe path for the vehicle through the surrounding sensed by various sensors of the vehicles. Sensory information can include data related to nearby vehicles, pedestrians, road edges, and other salient features to assess accident threat. Such control systems ideally operate only during instances of significant threat, i.e., the control system allows the driver of the vehicle to have full control of the vehicle in low threat situations, but override the driver actions to control the movement of the vehicle during high threat situations. See, e.g., a method described in U.S. 2010/0228427. During those high threat situations, the control of the vehicles is similar to the control of the autonomously driven vehicles.
A path planning suitable for autonomous driving has been considered by a number of different systems and/or methods. For example, a method of U.S. 2014/0207325 describes a path generation from a pre-assigned grid on the road to possibly a lane change or passing maneuver. In U.S. Pat. No. 8,038,062B2 the grid is not fixed and expanded during the algorithm execution, which however requires the solution of complex numerical problems to account for kinematic constraints. To avoid these problems, the method of U.S. Pat. No. 7,734,387 uses a grid search followed by a smoothing algorithm to account for kinematic constraints. The construction of the grid of points in the path planning algorithm is often very complex, and hence in U.S. Pat. No. 8,666,548B2 a randomized algorithm is proposed, which operates in the vehicle configuration space to obtain a path that is close to what the vehicle can execute, but not smooth, that is, the path has sharp corners that the vehicle cannot exactly execute.
All those methods ignore the previous actions of the driver. However, in semi-autonomous vehicles, the driver regains the control of the vehicles after the threat is reduced and needs to further control the vehicle according to the objectives indicated by currently ignored previous actions.